code: def test_data_augmentation(apply_n):
      effects = [
          [0.1, "lowpass", {"cutoff_freq": 1000, "Q": 0.707}],
          [0.1, "highpass", {"cutoff_freq": 3000, "Q": 0.707}],
          [
              0.1,
              "equalization",
              {"center_freq": 1000, "gain": 0, "Q": 0.707},
          ],
          [
              0.1,
              [
                  [0.3, "speed_perturb", {"factor": 0.9}],
                  [0.4, "speed_perturb", {"factor": 1.1}],
              ],
          ],
          [
              0.5,
              [
                  [
                      0.5,
                      "clipping",
                      {"min_quantile": 0.1, "max_quantile": 0.9},
                  ],
                  [0.5, "reverse", {}],
              ],
          ],
      ]
      data_aug = DataAugmentation(effects, apply_n)
      audio = torch.randn(1000)
      sr = 8000
      ret = data_aug(audio, sr)
  
file_path: espnet/test/espnet2/layers/test_augmentation.py
imports: import pytest
  import torch
  from espnet2.layers.augmentation import DataAugmentation, bandpass_filtering, bandreject_filtering, clipping, contrast, corrupt_phase, deemphasis, equalization_filtering, highpass_filtering, lowpass_filtering, pitch_shift, polarity_inverse, preemphasis, reverse, speed_perturb, time_stretch
label_desc: Use image node_id 16 for calling a global function with example usage: test_data_augmentation(apply_n) without return types